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Detection and Localization of Image Forgeries using Resampling Features and Deep Learning [article]

Jason Bunk, Jawadul H. Bappy, Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Arjuna Flenner, B.S. Manjunath, Shivkumar Chandrasekaran, Amit K. Roy-Chowdhury, Lawrence Peterson
2017 arXiv   pre-print
In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning.  ...  We compare the performance of detection/localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries.  ...  The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.  ... 
arXiv:1707.00433v1 fatcat:ztjjc3ompbhjrbnt4xo7l5ktfm

Detection and Localization of Image Forgeries Using Resampling Features and Deep Learning

Jason Bunk, Jawadul H. Bappy, Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Arjuna Flenner, B.S. Manjunath, Shivkumar Chandrasekaran, Amit K. Roy-Chowdhury, Lawrence Peterson
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning.  ...  We compare the performance of detection/localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries.  ...  A deep learning approach to identify facial retouching was proposed in [8] . In [42] , image region forgery detection has been performed using stacked auto-encoder model.  ... 
doi:10.1109/cvprw.2017.235 dblp:conf/cvpr/BunkBMNFMCRP17 fatcat:twryuybkanbmvplqiqm7kfmrki

Boosting Image Forgery Detection using Resampling Features and Copy-move analysis [article]

Tajuddin Manhar Mohammed, Jason Bunk, Lakshmanan Nataraj, Jawadul H. Bappy, Arjuna Flenner, B.S. Manjunath, Shivkumar Chandrasekaran, Amit K. Roy-Chowdhury, Lawrence Peterson
2018 arXiv   pre-print
We use the copy-move detection method as a pre-filtering step and pass those images that are classified as untampered to a deep learning based resampling detection framework.  ...  Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods.  ...  He has been on the organizing and program committees of multiple computer vision and image processing conferences and is serving on the editorial boards of multiple journals. Author Biography  ... 
arXiv:1802.03154v2 fatcat:3hfktyx6dbau5bw436d7oykdra

Boosting Image Forgery Detection using Resampling Features and Copy-move Analysis

Tajuddin Manhar Mohammed, Jason Bunk, Lakshmanan Nataraj, Jawadul H. Bappy, Arjuna Flenner, B.S. Manjunath, Shivkumar Chandrasekaran, Amit K. Roy-Chowdhury, Lawrence A. Peterson
2018 IS&T International Symposium on Electronic Imaging Science and Technology  
We use the copy-move detection method as a pre-filtering step and pass those images that are classified as untampered to a deep learning based resampling detection framework.  ...  Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods.  ...  He has been on the organizing and program committees of multiple computer vision and image processing conferences and is serving on the editorial boards of multiple journals.  ... 
doi:10.2352/issn.2470-1173.2018.07.mwsf-118 fatcat:7k3uikbtwfemxb4ktpn3byaznu

Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis [article]

Arjuna Flenner, Lawrence Peterson, Jason Bunk, Tajuddin Manhar Mohammed, Lakshmanan Nataraj, B.S. Manjunath
2018 arXiv   pre-print
In this paper we discuss a method to automatically detect local resampling using deep learning while controlling the false alarm rate using a-contrario analysis.  ...  A deep learning classifier is then used to generate a heatmap that indicates if the image block has been resampled. We expect some of these blocks to be falsely identified as resampled.  ...  The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.  ... 
arXiv:1803.01711v1 fatcat:yjs64koy3jdkzagvzmdxu5ap3u

Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis

A. Flenner, L. Peterson, J. Bunk, T.M. Mohammed, L. Nataraj, B.S. Manjunath
2018 IS&T International Symposium on Electronic Imaging Science and Technology  
In this paper we discuss a method to automatically detect local resampling using deep learning while controlling the false alarm rate using a-contrario analysis.  ...  A deep learning classifier is then used to generate a heatmap that indicates if the image block has been resampled. We expect some of these blocks to be falsely identified as resampled.  ...  The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.  ... 
doi:10.2352/issn.2470-1173.2018.07.mwsf-212 fatcat:snacz3wjkrgxdefx3tvlojuohu

AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI

Kalyani Kadam, Swati Ahirrao, Ketan Kotecha
2021 International Journal of Power Electronics and Drive Systems (IJPEDS)  
Experts have applied deep learning techniques to detect a forgery in the image too.  ...  Explainable AI (XAI) algorithms have been used to interpret a black box's decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches.  ...  Important words are copy move, splicing, deep learning, forgery detection, localization of forgery, segmentation, classification, visualization, explainable, and XAI.  ... 
doi:10.11591/ijece.v11i5.pp4489-4501 fatcat:jfxrbki7d5bjpb2i7fhn6e53fm

Boundary-based Image Forgery Detection by Fast Shallow CNN [article]

Zhongping Zhang, Yixuan Zhang, Zheng Zhou, Jiebo Luo
2018 arXiv   pre-print
Image forgery detection is the task of detecting and localizing forged parts in tampered images.  ...  Previous works mostly focus on high resolution images using traces of resampling features, demosaicing features or sharpness of edges.  ...  Many deep learning based methods [2] , [3] , [4] still utilize the resampling features in image forgery detection.  ... 
arXiv:1801.06732v2 fatcat:fqe7yofj2bdfbp7gtu2ge6pvem

Effect of Error Level Analysis on The Image Forgery Detection Using Deep Learning

Wina Permana Sari, Hisyam Fahmi
2021 Kinetik  
This study wants to know the effect of adding ELA extraction process in the image forgery detection using deep learning approach.  ...  The Convolutional Neural Network (CNN), which is a deep learning method, is used as a method to do the image forgery detection.  ...  Hopefully, this research can make a major contribution to the advancement of technology in Indonesia.  ... 
doi:10.22219/kinetik.v6i3.1272 fatcat:abelkvfe5zhrrh2oh7en5kckrq

Semantic Modeling and Pixel Discrimination for Image Manipulation Detection

Ziyu Xue, Xiuhua Jiang, Qingtong Liu, Beijing Chen
2022 Security and Communication Networks  
Specifically, the pixel-level detection branch resamples features and uses an LSTM to detect manipulations, such as resampling, rotation, and cropping.  ...  In this paper, the detection of image manipulation areas based on forgery object detection and pixel discrimination is proposed.  ...  Acknowledgments is work was supported by the Basic Scientific Research Operating Expenses of the Academy of Broadcasting Science, NRTA (No. JBKY2020006).  ... 
doi:10.1155/2022/9755509 fatcat:igf4spljqng7zbbfkffgfhi2c4

Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries [article]

Jawadul H. Bappy, Cody Simons, Lakshmanan Nataraj, B.S. Manjunath, Amit K. Roy-Chowdhury
2019 arXiv   pre-print
Resampling features are used to capture artifacts like JPEG quality loss, upsampling, downsampling, rotation, and shearing.  ...  Finally, decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization.  ...  A deep learning approach to identify facial retouching was proposed in [11] . In [88] , image region forgery detection has been performed using stacked auto-encoder model.  ... 
arXiv:1903.02495v1 fatcat:bu6iacqbhvbbxboebyfwwu2qne

Hybrid Algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors

Jimmy alexander Cortés Osorio, José Andrés Chaves Osorio, Cristian David López Robayo
2021 Revista Facultad de Ingeniería  
This investigation introduces an algorithm to detect the main types of pixel-based alterations such as copy-move forgery, resampling, and splicing in digital images.  ...  Of 7100 images evaluated, 3666 were unaltered, 791 had resampling, 2213 had splicing, and 430 had copy-move forgeries.  ...  As copy-move detection, a deep learning approach is used for splicing detection.  ... 
doi:10.17533/udea.redin.20211165 fatcat:zzyz3alykfd6hmhjzif6wxrlry

Efficient resampling features and convolution neural network model for image forgery detection

Manjunatha S, Malini M. Patil
2022 Indonesian Journal of Electrical Engineering and Computer Science  
This paper provides a competent tampering detection technique using resampling features and convolution neural network (CNN).  ...  The image forensic approach has been employed for detecting whether or not an image has been manipulated with the usage of positive attacks which includes splicing, and copy-move.  ...  EFFICIENT RSF AND CNN MODEL FOR IMAGE FORGERY DETECTION Here the tampering detection through resampling feature extraction and CNN descriptor is presented.  ... 
doi:10.11591/ijeecs.v25.i1.pp183-190 fatcat:bqbmlxb6fvejxnu2j4gtfke75e

Copy Move Forgery Detection Techniques: A Comprehensive Survey of Challenges and Future Directions

Ibrahim A. Zedan, Mona M. Soliman, Khaled M. Elsayed, Hoda M. Onsi
2021 International Journal of Advanced Computer Science and Applications  
Digital Image Forensics is a growing field of image processing that attempts to gain objective proof of the origin and veracity of a visual image.  ...  In this survey, we cover the conventional and the deep learning based CMFD techniques from a new perspective.  ...  Object detection networks such as R-CNN, and Faster R-CNN are able to localize the forgery using bounding boxes.  ... 
doi:10.14569/ijacsa.2021.0120729 fatcat:5kebiko6ojg3ppcp7htl5nnrrq

Universal Image Manipulation Detection using Deep Siamese Convolutional Neural Network [article]

Aniruddha Mazumdar, Jaya Singh, Yosha Singh Tomar, Prabin Kumar Bora
2018 arXiv   pre-print
It gives the information about the processing history of an image, and also can expose forgeries present in an image.  ...  To solve this problem, we propose a novel deep learning-based method which can differentiate between different types of image editing operations.  ...  This is the first deep learning-based image manipulation detection method, where the first layer computes the median filtering residual and the subsequent layers extract and classify the features useful  ... 
arXiv:1808.06323v2 fatcat:avr3wjzdmbdwngx2n4eio6h2xq
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